Singular Spectrum Analysis with R

Saved in:
Bibliographic Details
Main Author:
Corporate Author:
Other Authors:
Special Collection:e-book
Format: Book
Language:English
Published: Berlin, Heidelberg : : Springer Berlin Heidelberg : Imprint: Springer,, 2018
Edition:1st ed. 2018.
Series:Use R!,, ISSN 2197-5736
Subjects:
Online Access:https://doi.org/10.1007/978-3-662-57380-8
Tags: Add Tag
Be the first to tag this record!
id opac-EUL01-000979537
collection e-book
institution L_200
EUL01
spelling Golyandina, Nina. szerző aut http://id.loc.gov/vocabulary/relators/aut
Singular Spectrum Analysis with R by Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky.
1st ed. 2018.
Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2018
XIII, 272 p. 121 illus., 106 illus. in color. online forrás
szöveg txt rdacontent
számítógépes c rdamedia
távoli hozzáférés cr rdacarrier
szövegfájl PDF rda
Use R!, 2197-5736
Preface -- Common symbols and acronyms -- Contents -- 1 Introduction: Overview -- 2 SSA analysis of one-dimensional time series -- 3 Parameter estimation, forecasting, gap filling -- 4 SSA for multivariate time series -- 5 Image processing -- Index -- References.
This comprehensive and richly illustrated volume provides up-to-date material on Singular Spectrum Analysis (SSA). SSA is a well-known methodology for the analysis and forecasting of time series. Since quite recently, SSA is also being used to analyze digital images and other objects that are not necessarily of planar or rectangular form and may contain gaps. SSA is multi-purpose and naturally combines both model-free and parametric techniques, which makes it a very special and attractive methodology for solving a wide range of problems arising in diverse areas, most notably those associated with time series and digital images. An effective, comfortable and accessible implementation of SSA is provided by the R-package Rssa, which is available from CRAN and reviewed in this book. Written by prominent statisticians who have extensive experience with SSA, the book (a) presents the up-to-date SSA methodology, including multidimensional extensions, in language accessible to a large circle of users, (b) combines different versions of SSA into a single tool, (c) shows the diverse tasks that SSA can be used for, (d) formally describes the main SSA methods and algorithms, and (e) provides tutorials on the Rssa package and the use of SSA. The book offers a valuable resource for a very wide readership, including professional statisticians, specialists in signal and image processing, as well as specialists in numerous applied disciplines interested in using statistical methods for time series analysis, forecasting, signal and image processing. The book is written on a level accessible to a broad audience and includes a wealth of examples; hence it can also be used as a textbook for undergraduate and postgraduate courses on time series analysis and signal processing.
Nyomtatott kiadás: ISBN 9783662573785
Nyomtatott kiadás: ISBN 9783662573792
Az e-könyvek a teljes ELTE IP-tartományon belül online elérhetők.
könyv
e-book
Mathematical statistics.
Computer vision.
Computer software.
Statistics.
Statistical Theory and Methods.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Mathematical Software.
Statistics for Business, Management, Economics, Finance, Insurance.
Statistics and Computing/Statistics Programs.
Statistics for Life Sciences, Medicine, Health Sciences.
elektronikus könyv
Korobeynikov, Anton. szerző aut http://id.loc.gov/vocabulary/relators/aut
Zhigljavsky, Anatoly. szerző aut http://id.loc.gov/vocabulary/relators/aut
SpringerLink (Online service) közreadó testület
Online változat https://doi.org/10.1007/978-3-662-57380-8
EUL01
language English
format Book
author Golyandina, Nina., szerző
spellingShingle Golyandina, Nina., szerző
Singular Spectrum Analysis with R
Use R!,, ISSN 2197-5736
Mathematical statistics.
Computer vision.
Computer software.
Statistics.
Statistical Theory and Methods.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Mathematical Software.
Statistics for Business, Management, Economics, Finance, Insurance.
Statistics and Computing/Statistics Programs.
Statistics for Life Sciences, Medicine, Health Sciences.
elektronikus könyv
author_facet Golyandina, Nina., szerző
Korobeynikov, Anton., szerző
Zhigljavsky, Anatoly., szerző
SpringerLink (Online service)
author2 Korobeynikov, Anton., szerző
Zhigljavsky, Anatoly., szerző
author_corporate SpringerLink (Online service)
author_sort Golyandina, Nina.
title Singular Spectrum Analysis with R
title_short Singular Spectrum Analysis with R
title_full Singular Spectrum Analysis with R by Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky.
title_fullStr Singular Spectrum Analysis with R by Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky.
title_full_unstemmed Singular Spectrum Analysis with R by Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky.
title_auth Singular Spectrum Analysis with R
title_sort singular spectrum analysis with r
series Use R!,, ISSN 2197-5736
series2 Use R!,
publishDate 2018
publishDateSort 2018
physical XIII, 272 p. 121 illus., 106 illus. in color. : online forrás
edition 1st ed. 2018.
isbn 978-3-662-57380-8
issn 2197-5736
callnumber-first Q - Science
callnumber-subject QA - Mathematics
callnumber-label QA276-280
callnumber-raw 979537
callnumber-search 979537
topic Mathematical statistics.
Computer vision.
Computer software.
Statistics.
Statistical Theory and Methods.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Mathematical Software.
Statistics for Business, Management, Economics, Finance, Insurance.
Statistics and Computing/Statistics Programs.
Statistics for Life Sciences, Medicine, Health Sciences.
elektronikus könyv
topic_facet Mathematical statistics.
Computer vision.
Computer software.
Statistics.
Statistical Theory and Methods.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Mathematical Software.
Statistics for Business, Management, Economics, Finance, Insurance.
Statistics and Computing/Statistics Programs.
Statistics for Life Sciences, Medicine, Health Sciences.
elektronikus könyv
Mathematical statistics.
Computer vision.
Computer software.
Statistics.
Statistical Theory and Methods.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Mathematical Software.
Statistics for Business, Management, Economics, Finance, Insurance.
Statistics and Computing/Statistics Programs.
Statistics for Life Sciences, Medicine, Health Sciences.
url https://doi.org/10.1007/978-3-662-57380-8
illustrated Not Illustrated
dewey-hundreds 500 - Science
dewey-tens 510 - Mathematics
dewey-ones 519 - Probabilities & applied mathematics
dewey-full 519.5
dewey-sort 3519.5
dewey-raw 519.5
dewey-search 519.5
first_indexed 2021-04-02T14:47:08Z
last_indexed 2021-04-04T08:17:53Z
recordtype opac
publisher Berlin, Heidelberg : : Springer Berlin Heidelberg : Imprint: Springer,
_version_ 1696089584743481344
score 13,329224
generalnotes This comprehensive and richly illustrated volume provides up-to-date material on Singular Spectrum Analysis (SSA). SSA is a well-known methodology for the analysis and forecasting of time series. Since quite recently, SSA is also being used to analyze digital images and other objects that are not necessarily of planar or rectangular form and may contain gaps. SSA is multi-purpose and naturally combines both model-free and parametric techniques, which makes it a very special and attractive methodology for solving a wide range of problems arising in diverse areas, most notably those associated with time series and digital images. An effective, comfortable and accessible implementation of SSA is provided by the R-package Rssa, which is available from CRAN and reviewed in this book. Written by prominent statisticians who have extensive experience with SSA, the book (a) presents the up-to-date SSA methodology, including multidimensional extensions, in language accessible to a large circle of users, (b) combines different versions of SSA into a single tool, (c) shows the diverse tasks that SSA can be used for, (d) formally describes the main SSA methods and algorithms, and (e) provides tutorials on the Rssa package and the use of SSA. The book offers a valuable resource for a very wide readership, including professional statisticians, specialists in signal and image processing, as well as specialists in numerous applied disciplines interested in using statistical methods for time series analysis, forecasting, signal and image processing. The book is written on a level accessible to a broad audience and includes a wealth of examples; hence it can also be used as a textbook for undergraduate and postgraduate courses on time series analysis and signal processing.